Nova Micro 1.0 vs gpt-oss-120b
gpt-oss-120b leads Nova Micro 1.0 on the Artificial Analysis Intelligence Index, 11.6 to 5.9, and Nova Micro 1.0 costs 3.6x less per output token ($0.14 vs $0.50 per million). Updated October 21, 2026; 38 benchmarks have a result for both.
Nova Micro 1.0 or gpt-oss-120b: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nova Micro 1.0 for output tokens 3.6x cheaper ($0.14 vs $0.50 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 38
- Nova Micro 1.0 only
- 1
- gpt-oss-120b only
- 40
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
gpt-oss-120b
gpt-oss-120b leads, 11.6 against 5.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only gpt-oss-120b has a AA Agentic Index result, 3.7.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-120b has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-120b has a MMLU Pro result, 79.2%.
Directional only
Web design
Design Arena: websites
No call
Only gpt-oss-120b has a Design Arena: websites result, 968.
Directional only
Long documents
Listed context window
gpt-oss-120b
gpt-oss-120b takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nova Micro 1.0
Nova Micro 1.0 $0.0001, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only gpt-oss-120b has the number, $0.51.
Directional only
At a glance
- Coding index
- Nova Micro 1.0 not benchmarked, gpt-oss-120b not benchmarked.
- Widest category
- Math, 58.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): Nova Micro 1.0 6.0% against gpt-oss-120b 93.4%, 87.4 points apart on a 0 to 100 scale.
- Output price
- Nova Micro 1.0 $0.14 against gpt-oss-120b $0.50 per million output tokens, 3.6x apart.
- Context window
- Nova Micro 1.0 takes 128K tokens, gpt-oss-120b 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Nova Micro 1.0 | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 41.8% | Only gpt-oss-120b measured |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| AA Coding Index | Not measured | 30.4 | Only gpt-oss-120b measured |
| LiveCodeBench (Vals AI) | Not measured | 83.2% | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | Not measured | 33.6% | Only gpt-oss-120b measured |
| LiveCodeBench (Artificial Analysis run) | 14.0% | 87.8% | gpt-oss-120b |
| SciCode | Not measured | 34.0% | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | 23.5% | gpt-oss-120b |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| WeirdML | Not measured | 48.2% | Only gpt-oss-120b measured |
| ALE-Bench | Not measured | 575.6 | Only gpt-oss-120b measured |
| LMArena Text: coding | 1218 | 1379 | gpt-oss-120b |
| LMArena Text: software and IT | 1215 | 1387 | gpt-oss-120b |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.9 | 11.6 | gpt-oss-120b |
| Epoch Capabilities Index | Not measured | 139.8 | Only gpt-oss-120b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 3.7 | Only gpt-oss-120b measured |
| τ²-Bench Telecom | 14.0% | 65.8% | gpt-oss-120b |
| APEX-Agents | Not measured | 4.4% | Only gpt-oss-120b measured |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| Vending-Bench 2 | Not measured | -$22 | Only gpt-oss-120b measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | 35.8% | 78.2% | gpt-oss-120b |
| Humanity's Last Exam (Artificial Analysis run) | 4.6% | 19.6% | gpt-oss-120b |
| GPQA Diamond (Epoch AI run) | Not measured | 75.8% | Only gpt-oss-120b measured |
| SimpleBench | Not measured | 22.1% | Only gpt-oss-120b measured |
| Mystery game puzzles | Not measured | 2.0% | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | 1191 | 1364 | gpt-oss-120b |
| LMArena Text: expert | 1183 | 1356 | gpt-oss-120b |
| LMArena Text: hard prompts in English | 1203 | 1368 | gpt-oss-120b |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | 6.0 | 93.4 | gpt-oss-120b |
| AIME 2025 (Artificial Analysis run) | 6.0% | 93.4% | gpt-oss-120b |
| OTIS Mock AIME | Not measured | 88.9% | Only gpt-oss-120b measured |
| LMArena Text: math | 1206 | 1389 | gpt-oss-120b |
| LMArena Text: mathematical work | 1212 | 1386 | gpt-oss-120b |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.2% | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | Not measured | 75.9% | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | Not measured | 58.2% | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | Not measured | 71.6% | Only gpt-oss-120b measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MMLU Pro (Artificial Analysis run) | 53.1% | 80.8% | gpt-oss-120b |
| MMLU | 70.8% | Not measured | Only Nova Micro 1.0 measured |
| LMArena Text: science | 1210 | 1382 | gpt-oss-120b |
| LMArena Text: medicine and healthcare | 1207 | 1388 | gpt-oss-120b |
| LMArena Text: legal and government | 1221 | 1355 | gpt-oss-120b |
| LMArena Text: business and finance | 1182 | 1359 | gpt-oss-120b |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 968 | Only gpt-oss-120b measured |
| Design Arena: UI components | Not measured | 927 | Only gpt-oss-120b measured |
| Design Arena: game dev | Not measured | 992 | Only gpt-oss-120b measured |
| Design Arena: data viz | Not measured | 987 | Only gpt-oss-120b measured |
| Design Arena: 3D | Not measured | 901 | Only gpt-oss-120b measured |
| Design Arena: all code categories | Not measured | 963 | Only gpt-oss-120b measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| IFBench | 29.4% | 69.0% | gpt-oss-120b |
| Long context reasoning (AA-LCR) | 13.0% | 52.0% | gpt-oss-120b |
| DTBench | Not measured | 76.3% | Only gpt-oss-120b measured |
| LMCA | Not measured | 22.1% | Only gpt-oss-120b measured |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | Not measured | 25.0% | Only gpt-oss-120b measured |
| LMArena Text | 1208 | 1366 | gpt-oss-120b |
| LMArena Text: instruction following | 1174 | 1318 | gpt-oss-120b |
| LMArena Text: creative writing | 1172 | 1274 | gpt-oss-120b |
| LMArena Text: English | 1224 | 1375 | gpt-oss-120b |
| LMArena Text: non-English | 1186 | 1351 | gpt-oss-120b |
| LMArena Text: Chinese | 1209 | 1385 | gpt-oss-120b |
| LMArena Text: German | 1192 | 1353 | gpt-oss-120b |
| LMArena Text: Japanese | 1154 | 1331 | gpt-oss-120b |
| LMArena Text: Korean | 1150 | 1282 | gpt-oss-120b |
| LMArena Text: French | 1238 | 1370 | gpt-oss-120b |
| LMArena Text: Spanish | 1225 | 1389 | gpt-oss-120b |
| LMArena Text: Russian | 1185 | 1343 | gpt-oss-120b |
| LMArena Text: Polish | Not measured | 1353 | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | 1178 | 1340 | gpt-oss-120b |
| LMArena Text: longer queries | 1205 | 1319 | gpt-oss-120b |
| LMArena Text: writing and language | 1192 | 1306 | gpt-oss-120b |
| LMArena Text: entertainment and media | 1163 | 1292 | gpt-oss-120b |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nova Micro 1.0 | gpt-oss-120b | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Nova Micro 1.0 costs less
Repository review
50K input + 3K output tokens
Nova Micro 1.0 costs less
Agent loop
200K input + 10K output tokens
Nova Micro 1.0 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Amazon Nova Micro 1.0 | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | Amazon | OpenAI |
| Weights | No public weights listed | Open weights |
| Released | December 5, 2024 | August 5, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $0.035 | $0.10 |
| Output $/Mtok | $0.14 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nova Micro 1.0 better than gpt-oss-120b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nova Micro 1.0 for output tokens 3.6x cheaper ($0.14 vs $0.50 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Nova Micro 1.0 or gpt-oss-120b?
Nova Micro 1.0 costs $0.035 per million input tokens and $0.14 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, Nova Micro 1.0 or gpt-oss-120b?
gpt-oss-120b, with 131K tokens against 128K.
How many benchmarks is the Nova Micro 1.0 vs gpt-oss-120b comparison based on?
38 benchmarks have a result for both models as of October 21, 2026. Nova Micro 1.0's coding index rests on 0 independent benchmarks and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.